Big Data Technology Comprehensive Training Lab
Introduction
The primary focus of the large model training lab lies in leveraging large models and retrieval enhancement generation, combined with a vast corpus, to achieve intelligent retrieval and generation of domain-specific knowledge. This ultimately enhances the efficiency and accuracy of knowledge application. The training lab facilitates multimodal understanding by analyzing both uploaded images and texts provided by users. It responds to user queries related to the uploaded materials, providing comprehensive answers. Furthermore, it undertakes the collection, organization, creation, training, and application of knowledge bases. Specific implementation includes large language models (LLM), retrieval enhancement generation (RAG), construction and training of knowledge base systems - all supporting various roles such as artificial intelligence trainers and developers.
Enterprise positions: artificial intelligence application development engineer
Applicable majors: artificial intelligence engineering technology/computer related majors
Course products: deep learning, natural language, AI large model foundation and application development, LangChain foundation and application development
Project products: intelligent question and answer system based on large model Web, Qwen3B and ChatGLM3-6B model installation, deployment and actual measurement
Applicable scenarios: professional teaching, comprehensive training, competition training
Feature
Keep up with the latest trends
The project is centered around two cutting-edge topics: large-scale models and Artificial Intelligence in General Context (AIGC), and it is being practically implemented, encompassing the practical application of Language Model with Large-scale knowledge base (LLM) combined with Retrieval-Augmented Generation (RAG), as well as the real-world application scenario for enterprises based on a knowledge base Agent.
Wide technical coverage
The core technology incorporates the latest and widely adopted LLM (Large Language Model), RAG (Retrieval Enhanced Generation), Embedding, Vue, and SpringBoot frameworks. It is designed to be encapsulated-friendly and loosely coupled, facilitating an easy learning experience for students.
Diversified scenarios
The system not only facilitates practical teaching for teachers but also serves as an educational assistant, providing round-the-clock answers to students' common questions regarding courses and practical training. Additionally, it offers online customer service with automated responses to inquiries, enhancing service efficiency and user satisfaction. Moreover, it extends its support by offering psychological counseling services to understand and address users' emotional needs.
The primary focus of the large model training lab lies in leveraging large models and retrieval enhancement generation, combined with a vast corpus, to achieve intelligent retrieval and generation of domain-specific knowledge. This ultimately enhances the efficiency and accuracy of knowledge application. The training lab facilitates multimodal understanding by analyzing both uploaded images and texts provided by users. It responds to user queries related to the uploaded materials, providing comprehensive answers. Furthermore, it undertakes the collection, organization, creation, training, and application of knowledge bases. Specific implementation includes large language models (LLM), retrieval enhancement generation (RAG), construction and training of knowledge base systems - all supporting various roles such as artificial intelligence trainers and developers.
Enterprise positions: artificial intelligence application development engineer
Applicable majors: artificial intelligence engineering technology/computer related majors
Course products: deep learning, natural language, AI large model foundation and application development, LangChain foundation and application development
Project products: intelligent question and answer system based on large model Web, Qwen3B and ChatGLM3-6B model installation, deployment and actual measurement
Applicable scenarios: professional teaching, comprehensive training, competition training
Feature
Keep up with the latest trends
The project is centered around two cutting-edge topics: large-scale models and Artificial Intelligence in General Context (AIGC), and it is being practically implemented, encompassing the practical application of Language Model with Large-scale knowledge base (LLM) combined with Retrieval-Augmented Generation (RAG), as well as the real-world application scenario for enterprises based on a knowledge base Agent.
Wide technical coverage
The students' skills are enhanced in various areas, including operating systems, containers, large models, RAG (Retrieval-Augmented Generation), application software, front-end UI development, docker principles and deployment, vector databases, knowledge base application engineering. This strengthens their ability to integrate theoretical knowledge with practical experience.
The core technology incorporates the latest and widely adopted LLM (Large Language Model), RAG (Retrieval Enhanced Generation), Embedding, Vue, and SpringBoot frameworks. It is designed to be encapsulated-friendly and loosely coupled, facilitating an easy learning experience for students.
Diversified scenarios
The system not only facilitates practical teaching for teachers but also serves as an educational assistant, providing round-the-clock answers to students' common questions regarding courses and practical training. Additionally, it offers online customer service with automated responses to inquiries, enhancing service efficiency and user satisfaction. Moreover, it extends its support by offering psychological counseling services to understand and address users' emotional needs.
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